A Stock market collapse occurs when stock prices drop by more than 10% across all main indexes. P... more A Stock market collapse occurs when stock prices drop by more than 10% across all main indexes. Predicting a stock market crisis is difficult because of the increased volatility in the stock market. Stock price drops can be triggered by a variety of factors, including corporate results, geopolitical tensions, financial crises, and pandemic events. For scholars and investors, predicting a crisis is a difficult endeavor. We developed a model for the prediction of stock crisis using Hybridized Feature Selection (HFS) approach. Firstly, we went for the suggestion of the HFS method for the removal of stock’s unnecessary financial attributes. The Naïve Bayes approach, on the other hand, is used for the classification of strong fundamental stocks. In the third step, Stochastic Relative Strength Index (StochRSI) is employed to identify a stock price bubble. In the fourth step, we identified the stock market crisis point in stock prices through moving average statistics. The fifth is the pre...
Motivation Many real applications such as businesses and health generate large categorical datase... more Motivation Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clustering analysis algorithms do not provide a suitable means for dealing with categorical data, uncertainty, and stability. Problem statement The ability of decision making in the presence of vagueness and uncertainty in data can be handled using Rough Set Theory. Though, recent categorical clustering techniques based on Rough Set Theory help but they suffer from low accuracy, high computational complexity, and generalizability especially on data sets where they sometimes fail or hardly select their best clustering attribute. Objectives The main objective of this research is to propose a new information theoretic based Rough Purity Approach...
2014 International Joint Conference on Neural Networks (IJCNN), 2014
This paper presents a novel application of Recurrent HONN to forecast the future index of tempera... more This paper presents a novel application of Recurrent HONN to forecast the future index of temperature time series data. The prediction capability of Recurrent HONN, namely the Recurrent Pi-Sigma Neural Network was tested on a five-year temperature data taken from Batu Pahat, Malaysia. The performance of the network is benchmarked against the performance of Multilayer Perceptron, and the standard Pi-Sigma Neural Network. The predictions demonstrated that Recurrent Pi-Sigma Neural Network is capable in predicting the future index of temperature series in comparison to other models. It is observed that the network is able to find an appropriate input output mapping of the chaotic temperature signals with a good performance in learning speed and generalization capability.
Question classification is one of the essential tasks for automatic question answering implementa... more Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word em...
International Journal of Modern Physics: Conference Series, 2012
In this paper, we present the effect of network parameters to forecast temperature of a suburban ... more In this paper, we present the effect of network parameters to forecast temperature of a suburban area in Batu Pahat, Johor. The common ways of predicting the temperature using Neural Network has been applied for most meteorological parameters. However, researchers frequently neglected the network parameters which might affect the Neural Network's performance. Therefore, this study tends to explore the effect of network parameters by using Pi Sigma Neural Network (PSNN) with backpropagation algorithm. The network's performance is evaluated using the historical dataset of temperature in Batu Pahat for one step-ahead and benchmarked against Multilayer Perceptron (MLP) for comparison. We found out that, network parameters have significantly affected the performance of PSNN for temperature forecasting. Towards the end of this paper, we concluded the best forecasting model to predict the temperature based on the comparison of our study.
Communications in Computer and Information Science, 2011
In this study, two artificial neural network (ANN) models, a Pi-Sigma Neural Network (PSNN) and a... more In this study, two artificial neural network (ANN) models, a Pi-Sigma Neural Network (PSNN) and a three-layer multilayer perceptron (MLP), are applied for temperature forecasting. PSNN is use to overcome the limitation of widely used MLP, which can easily get stuck into local minima and prone to overfitting. Therefore, good generalisation may not be obtained. The models were trained with backpropagation algorithm on historical temperature data of Batu Pahat region. Through 810 experiments, we found that PSNN performs considerably ...
International Journal of Computational Intelligence and Applications, 2014
The main purpose of this study is to employ Pi-Sigma Neural Network (PSNN) for one-step-ahead tem... more The main purpose of this study is to employ Pi-Sigma Neural Network (PSNN) for one-step-ahead temperature forecasting. In this paper, we evaluate the performances of PSNN by comparing the network model with widely used Multilayer Perceptron (MLP). PSNN which is a class of Higher Order Neural Networks (HONN), has a highly regular structure, needs much smaller number of weights and less training time. The PSNN is use to overcome the drawbacks of MLP, which can easily trapped into local minima and prone to overfit. Both network models were trained with standard backpropagation algorithm. Through 1012 experiments, it has been demonstrated that the PSNN has a high practicability and better temperature forecasting for one-step-ahead using historical temperature data of Batu Pahat region.
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services - iiWAS '11, 2011
Abstract This paper presents the application of a combined approach of Higher Order Neural Networ... more Abstract This paper presents the application of a combined approach of Higher Order Neural Networks and Recurrent Neural Networks, so called Jordan Pi-Sigma Neural Network (JPSN) for comprehensive temperature forecasting. In the present study, one-step-ahead forecasts are made for daily temperature measurement, by using a 5-year historical temperature measurement data. We also examine the effects of network parameters viz the learning factors, the higher order terms and the number of neurons in the input layer for ...
International Journal of Intelligent Systems and Applications
The standard method to train the Higher Order Neural Networks (HONN) is the well-known Backpropag... more The standard method to train the Higher Order Neural Networks (HONN) is the well-known Backpropagation (BP) algorithm. Yet, the current BP algorithm has several limitations including easily stuck into local minima, particularly when dealing with highly non-linear problems and utilise computationally intensive training algorithms. The current BP algorithm is also relying heavily on the initial weight values and other parameters picked. Therefore, in an attempt to overcome the BP drawbacks, we investigate a method called Modified Cuckoo Search-Markov chain Monté Carlo for optimising the weights in HONN and boost the learning process. This method, which lies in the Swarm Intelligence area, is notably successful in optimisation task. We compared the performance with several HONN-based network models and standard Multilayer Perceptron on four (4) time series datasets: Temperature, Ozone, Gold Close Price and Bitcoin Closing Price from various repositories. Simulation results indicate tha...
AWERProcedia Information Technology and Computer Science, Dec 24, 2012
This paper presents an optimal higher order to forecast temperature event in Batu Pahat, Malaysia... more This paper presents an optimal higher order to forecast temperature event in Batu Pahat, Malaysia by using a Jordan Pi-Sigma Neural Network (JPSN). There are many conventional techniques in dealing with forecasting meteorological issue; however, there are some shortcoming noticed in terms of accuracy and tractability. To solve these problems, we consider evaluating the effects of higher order terms in JPSN for temperature forecasting. The data of temperature measurement in Batu Pahat has been used in order to validate ...
A Stock market collapse occurs when stock prices drop by more than 10% across all main indexes. P... more A Stock market collapse occurs when stock prices drop by more than 10% across all main indexes. Predicting a stock market crisis is difficult because of the increased volatility in the stock market. Stock price drops can be triggered by a variety of factors, including corporate results, geopolitical tensions, financial crises, and pandemic events. For scholars and investors, predicting a crisis is a difficult endeavor. We developed a model for the prediction of stock crisis using Hybridized Feature Selection (HFS) approach. Firstly, we went for the suggestion of the HFS method for the removal of stock’s unnecessary financial attributes. The Naïve Bayes approach, on the other hand, is used for the classification of strong fundamental stocks. In the third step, Stochastic Relative Strength Index (StochRSI) is employed to identify a stock price bubble. In the fourth step, we identified the stock market crisis point in stock prices through moving average statistics. The fifth is the pre...
Motivation Many real applications such as businesses and health generate large categorical datase... more Motivation Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clustering analysis algorithms do not provide a suitable means for dealing with categorical data, uncertainty, and stability. Problem statement The ability of decision making in the presence of vagueness and uncertainty in data can be handled using Rough Set Theory. Though, recent categorical clustering techniques based on Rough Set Theory help but they suffer from low accuracy, high computational complexity, and generalizability especially on data sets where they sometimes fail or hardly select their best clustering attribute. Objectives The main objective of this research is to propose a new information theoretic based Rough Purity Approach...
2014 International Joint Conference on Neural Networks (IJCNN), 2014
This paper presents a novel application of Recurrent HONN to forecast the future index of tempera... more This paper presents a novel application of Recurrent HONN to forecast the future index of temperature time series data. The prediction capability of Recurrent HONN, namely the Recurrent Pi-Sigma Neural Network was tested on a five-year temperature data taken from Batu Pahat, Malaysia. The performance of the network is benchmarked against the performance of Multilayer Perceptron, and the standard Pi-Sigma Neural Network. The predictions demonstrated that Recurrent Pi-Sigma Neural Network is capable in predicting the future index of temperature series in comparison to other models. It is observed that the network is able to find an appropriate input output mapping of the chaotic temperature signals with a good performance in learning speed and generalization capability.
Question classification is one of the essential tasks for automatic question answering implementa... more Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word em...
International Journal of Modern Physics: Conference Series, 2012
In this paper, we present the effect of network parameters to forecast temperature of a suburban ... more In this paper, we present the effect of network parameters to forecast temperature of a suburban area in Batu Pahat, Johor. The common ways of predicting the temperature using Neural Network has been applied for most meteorological parameters. However, researchers frequently neglected the network parameters which might affect the Neural Network's performance. Therefore, this study tends to explore the effect of network parameters by using Pi Sigma Neural Network (PSNN) with backpropagation algorithm. The network's performance is evaluated using the historical dataset of temperature in Batu Pahat for one step-ahead and benchmarked against Multilayer Perceptron (MLP) for comparison. We found out that, network parameters have significantly affected the performance of PSNN for temperature forecasting. Towards the end of this paper, we concluded the best forecasting model to predict the temperature based on the comparison of our study.
Communications in Computer and Information Science, 2011
In this study, two artificial neural network (ANN) models, a Pi-Sigma Neural Network (PSNN) and a... more In this study, two artificial neural network (ANN) models, a Pi-Sigma Neural Network (PSNN) and a three-layer multilayer perceptron (MLP), are applied for temperature forecasting. PSNN is use to overcome the limitation of widely used MLP, which can easily get stuck into local minima and prone to overfitting. Therefore, good generalisation may not be obtained. The models were trained with backpropagation algorithm on historical temperature data of Batu Pahat region. Through 810 experiments, we found that PSNN performs considerably ...
International Journal of Computational Intelligence and Applications, 2014
The main purpose of this study is to employ Pi-Sigma Neural Network (PSNN) for one-step-ahead tem... more The main purpose of this study is to employ Pi-Sigma Neural Network (PSNN) for one-step-ahead temperature forecasting. In this paper, we evaluate the performances of PSNN by comparing the network model with widely used Multilayer Perceptron (MLP). PSNN which is a class of Higher Order Neural Networks (HONN), has a highly regular structure, needs much smaller number of weights and less training time. The PSNN is use to overcome the drawbacks of MLP, which can easily trapped into local minima and prone to overfit. Both network models were trained with standard backpropagation algorithm. Through 1012 experiments, it has been demonstrated that the PSNN has a high practicability and better temperature forecasting for one-step-ahead using historical temperature data of Batu Pahat region.
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services - iiWAS '11, 2011
Abstract This paper presents the application of a combined approach of Higher Order Neural Networ... more Abstract This paper presents the application of a combined approach of Higher Order Neural Networks and Recurrent Neural Networks, so called Jordan Pi-Sigma Neural Network (JPSN) for comprehensive temperature forecasting. In the present study, one-step-ahead forecasts are made for daily temperature measurement, by using a 5-year historical temperature measurement data. We also examine the effects of network parameters viz the learning factors, the higher order terms and the number of neurons in the input layer for ...
International Journal of Intelligent Systems and Applications
The standard method to train the Higher Order Neural Networks (HONN) is the well-known Backpropag... more The standard method to train the Higher Order Neural Networks (HONN) is the well-known Backpropagation (BP) algorithm. Yet, the current BP algorithm has several limitations including easily stuck into local minima, particularly when dealing with highly non-linear problems and utilise computationally intensive training algorithms. The current BP algorithm is also relying heavily on the initial weight values and other parameters picked. Therefore, in an attempt to overcome the BP drawbacks, we investigate a method called Modified Cuckoo Search-Markov chain Monté Carlo for optimising the weights in HONN and boost the learning process. This method, which lies in the Swarm Intelligence area, is notably successful in optimisation task. We compared the performance with several HONN-based network models and standard Multilayer Perceptron on four (4) time series datasets: Temperature, Ozone, Gold Close Price and Bitcoin Closing Price from various repositories. Simulation results indicate tha...
AWERProcedia Information Technology and Computer Science, Dec 24, 2012
This paper presents an optimal higher order to forecast temperature event in Batu Pahat, Malaysia... more This paper presents an optimal higher order to forecast temperature event in Batu Pahat, Malaysia by using a Jordan Pi-Sigma Neural Network (JPSN). There are many conventional techniques in dealing with forecasting meteorological issue; however, there are some shortcoming noticed in terms of accuracy and tractability. To solve these problems, we consider evaluating the effects of higher order terms in JPSN for temperature forecasting. The data of temperature measurement in Batu Pahat has been used in order to validate ...
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